Script for imdb file conversion

DUDE_imdb_loader.py

@@ -282,14 +282,11 @@ if __name__ == "__main__":
     splits = dataset.keys()
 
     for split in splits:
-        if split != "val":
-            continue
-
         split_indices = []
         OCR_paths = []
         document_paths = []
         for i, x in enumerate(dataset[split]):
-            if x["data_split"] != split:
+            if x["data_split"] != split: #redundant check
                 continue
             if x["document"] not in document_paths:
                 document_paths.append(x["document"])
@@ -306,7 +303,7 @@ if __name__ == "__main__":
             documents_metadata = load_json(documents_metadata_filename)
         else:
             documents_metadata = {}
-            num_jobs = 1
+            num_jobs = 6
             block_size = int(len(document_paths) / num_jobs) + 1
             print(f"{block_size} * {num_jobs} = {block_size*num_jobs} ({len(document_paths)})")
             document_blocks = [
@@ -332,7 +329,7 @@ if __name__ == "__main__":
         # 2. Process OCR to obtain doc_ocr_info
         documents_ocr_filename = f"{split}-documents_ocr.json"
 
-        if os.path.exists(documents_ocr_filename) and False:
+        if os.path.exists(documents_ocr_filename):
             print(f"Loading from disk: {documents_ocr_filename}")
             documents_ocr_info = load_json(documents_ocr_filename)
         else:
@@ -362,65 +359,18 @@ if __name__ == "__main__":
 
             save_json(documents_ocr_filename, documents_ocr_info)
 
-        imdb = create_imdb_from_json(
-            dataset[split],  # .select(split_indices),
-            documents_metadata=documents_metadata,
-            documents_ocr_info=documents_ocr_info,
-            split=split,
-            version="0.1",
-            include_answers=True,
-        )
-        np.save(f"{split}_imdb.npy", imdb)  # dump to lerna
-
-    import pdb
-
-    pdb.set_trace()  # breakpoint 930f4f6a //
-
-    # page_image_dir = '/'.join(dataset['val']['document'][0].split("/")[:-1]).replace('PDF', 'images')
-    # if not os.path.exists(page_image_dir):
-    #     os.makedirs(page_image_dir)
-    # dataset.info.features
-
-    """
-    Describe all steps that need to happen after loading HF DUDE dataset
-    Change functions 
-    
-
-    page_images_dir
-
-
-    
-    2. Process OCR to obtain doc_ocr_info 
-    """
-    # update dataset with
-    # for split in SPLITS
-
-    # documents_metadata
-    # doc_ocr_info
-
-    # dict to unique docs
-    # documents_metadata[docId] = {"num_pages": num_pages, "page_image_names": image_names}
-    # doc_ocr_info[docId] = {"ocr_tokens": ocr_tokens, "ocr_boxes": ocr_boxes}
-
-    """
-    train_imdb = create_imdb_from_json(
-        train_data,
-        documents_metadata=documents_metadata,
-        documents_ocr_info=doc_ocr_info,
-        split="train",
-        version="0.1",
-        include_answers=True,
-    )
-
+        imdb_filename = f"{split}_imdb.npy"
+        if os.path.exists(imdb_filename):
+            print(f"Loading from disk: {imdb_filename}")
+            imdb = np.load(imdb_filename)
 
-    np.save("Imdb/train_imdb.npy", train_imdb)
-
-    document_paths = []
-    num_jobs = 6
-    block_size = int(len(document_ids) / num_jobs) + 1
-    print(f"{block_size} * {num_jobs} = {block_size*num_jobs} ({len(document_ids)})")
-    parallel_results = Parallel(n_jobs=num_jobs)(
-        delayed(get_document_metadata_block)(documents_metadata, documents, documents_blocks[i])
-        for i in range(num_jobs)
-    )
-    """
+        else:
+            imdb = create_imdb_from_json(
+                dataset[split],  # .select(split_indices),
+                documents_metadata=documents_metadata,
+                documents_ocr_info=documents_ocr_info,
+                split=split,
+                version="0.1",
+                include_answers=True,
+            )
+            np.save(imdb_filename, imdb)